Modern information processing environments employ large databases for storing and organizing ever increasing quantities of data. In a conventional information processing environment, multidimensional databases are often employed for storing large sets of data in a normalized (organized, indexed and/or keyed) form. Often, the data stored in a conventional multidimensional database is sparse, meaning that the data points, or values, are scattered and much of the effective data storage area is null, or zero data. Accordingly, typical multidimensional databases are adapted to store vast quantities of data by adapting to sparsity of the data stored thereby and efficiently enumerating the null or zero portions. In this manner, a conventional multidimensional database (DB) may store much more data than a static arrangement which allocates storage for every potential data point, or value, regardless of whether it represents a null value.
In a managed information environment including a storage area network (SAN), for example, there are many file objects, or file entities, stored in multiple storage arrays. Each of the storage arrays is overseen and managed by an agent executing, or running, on a host computer, or host. Accordingly, in a typical SAN, there may be many nodes each executing multiple agents. The agents are responsive to a SAN management application, such as a Simple Network Management Protocol application or other suitable control program, executing on a server node connected to the other nodes in the SAN. Due to the large number of collective file objects (files) in the SAN, a collection of file attributes presented by the aggregate file entries in a large SAN lends itself well to such an OLAP database, or so called “datacube” representation.